Developmental psychology is an important research area
to gain an understanding on how we acquire cognitive skills
starting from first imitation tasks to controlled motor acts, from
first babbling to words and sentences to finally reasoning,
consciousness and empathy (theory of mind). In contrast to
deep networks being trained on billions of e.g. images for
object recognition, infants learn rather slow and are provided
only with a very limited excerpt of the world. DNNs tuned
for benchmarking as was carried out during the past years
is very different from developmental learning and the ac-
quisition of cognitive skills. Although their performance on
speech recognition tasks is indubitable, DNN models show
low performance when the speech resources are getting noisy
(e.g. repetitions) or are from children, since their language
differs from adults.
In addition, the way children learn is driven by high intrinsic
motivation [1]}, curiosity [2]} and their physical interaction with
the world (embodiment). The latter was shown to be essential
in exploring affordances for the associative learning between
objects and grasp types [3]}, keeping also into account parental
scaffolding [4]}. The link between motor activation in the brain
and language acquisition speaks in favor of embodied learning
which demands to ground experiments in real robots (see e.g.
[5]}) rather than a set of labeled data.
Until now, these important aspects to acquire cognitive skills
are absent in learning systems based on DNNs which may
be explained by their difficulty in interpretation. A literature
research on this topic revealed that only a few trends emerged
towards closing this gap. One approach used a generative
deep neural network based on the time course of learning
object categories in children [6]}. Essentially, the authors
demonstrated that despite using linear neurons, their model
was able to capture the refinement of semantic labels and with
an additional analysis on the temporal dynamics of gradient-
based learning, that this differentiation emerges naturally in
hierarchical networks.
Another recent paper [7]} identified a gap between de-
velopmental learning and deep neural networks along the
hierarchical processing of object recognition on the perceptual
level for the acquisition of action knowledge on the cognitive
level where affordances play a crucial role. In the light of
embodied cognition, affordances are grounded in sensorimotor
experiences. The authors [7]} argued in favor of unsupervised
learning techniques as labels of sensorimotor experiences do
not exist. Though deep networks do not allow for flexible,
online adaptation of e.g. grasping when affordances may
change (think about the change of perspective of a mug
handle), the main argument integrating deep unsupervised
models is that they can serve as an enhancement strategy for
learned sensorimotor experience as in humans during sleep.
In conclusion, although still other developmental learning
mechanisms as the mentioned curiosity are missing and can
not be captured by DNNs, they might be a useful tool for
representational learning, encouraging a novel research area
called "deep developmental learning" [7]}.
Regarding the interpretability issues in DNN, a deep net-
work in a one-shot word learning scenario was employed and
the results investigated by means of methods from cognitive
psychology, namely the "cognitive bias" which allows children
to eliminate word hypotheses from a vast possible word space
[10]}. The network evaluation, which used training on the
popular ImageNet database, underpinned the “shape bias"
hypothesis, i.e. that humans tend to label objects similar in
shape as one entity. Joining DNNs with cognitive psychology
may open the way to further investigate the positive as well as
negative aspects of biases in human learning, complementing
behavioral studies which found the basis for many cognitive
robotic scenarios.
